With the proliferation of broadband multimedia access networks, there has been an increasing need for effective ways to monitor the perceptual quality of video information, communications, and entertainment (also referred to herein as a/the “quality of experience” or “QoE”). The International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) has standardized a computational model for estimating such QoE. Known as ITU-T Recommendation G.1070 “Opinion model for video-telephony applications,” or the “G.1070 model,” this computational model can be employed as a QoE planning tool for use in estimating the effects on QoE of variations in one or more quality parameters, including coding bit rate parameters, video frame rate parameters, packet loss rate parameters, loudness parameters, echo parameters, video resolution parameters, etc. Specifically, the G.1070 model includes three distinct models, namely, a speech quality estimation model, a video quality estimation model, and a multimedia quality integration model. The G.1070 video quality estimation model can be used to estimate the subjective effects on QoE of the quality parameters relating to video, such as the coding bit rate parameter, the video frame rate parameter, the packet loss rate parameter, the video resolution parameter, etc. For example, given assumptions about the coding bit rate, the frame rate, and the packet loss rate, the G.1070 video quality estimation model can be used to generate an estimate, typically in the form of a quality score, of the perceptual quality of the video that is delivered to the end user. Assuming a constant frame rate and a packet loss rate of zero, the G.1070 video quality estimation model typically produces higher quality scores for higher bit rates of compressed video information, and lower quality scores for lower bit rates of compressed video information.
Although the G.1070 model has been successfully employed as a QoE planning tool, the G.1070 model has drawbacks in that its conventional mode of operation is unsuitable for use as a QoE monitoring tool. As discussed above, given assumptions about the coding bit rate and the frame rate of compressed video information, and further, given assumptions about the packet loss rate of an associated video channel, the G.1070 video quality estimation model can generate an estimate in the form of a quality score of the perceptual quality of the video that is delivered to the end user. However, the coding bit rate parameter and the video frame rate parameter employed by the G.1070 video quality estimation model typically represent the coding bit rate and the frame rate, respectively, at which an encoded input video bitstream (also referred to herein as a/the “input bitstream”) is to be encoded for playback to the end user. Such coding bit rate parameters and video frame rate parameters employed by the G.1070 video quality estimation model do not generally represent the actual coding bit rate and the actual frame rate, respectively, of the input bitstream prior to decoding. Similarly, the packet loss rate parameter employed by the G.1070 video quality estimation model does not generally represent the actual packet loss rate of the video channel carrying the input bitstream, but instead typically represents the expected packet loss rate of the video channel to be used to deliver the video to the end user. Although information about the actual coding bit rate, the actual frame rate, and the actual packet loss rate of input bitstreams is generally not employed in QoE planning applications, such information can be useful for monitoring the QoE of transcoded bitstreams. Moreover, although the G.1070 model is generally suitable for estimating aspects of the perceptual quality of video that are related to the network, such as the expected packet loss rate, information about the content of the video is generally not considered in the G.1070 video quality estimation model. For example, a video scene may include a relatively complex background with a high level of motion, whereas the next subsequent video scene may include a relatively simple background with little or no motion. Each video frame of the next subsequent video scene may therefore be readily predictable from a reference video frame, and the coding bit rate required to achieve high quality coding of the next subsequent video scene may be relatively low. However, because the G.1070 video quality estimation model typically produces lower quality scores for lower bit rates of compressed video information, the G.1070 model may produce a relatively low quality score for the next subsequent video scene, notwithstanding the fact that the perceptual quality of that video scene may actually be high, perhaps even higher than the perceptual quality of the video scene that preceded it. Moreover, another such video scene may be a very complex video scene, but the instantaneous coding bit rate required to represent the complex video scene with high quality may exceed the capabilities of the video channel and/or the video decoder. In that case, a bit rate control algorithm implemented in the video encoder may operate to limit the coding bit rate at a relatively high level, but not high enough to assure high quality coding of the complex video scene. Because the G.1070 video quality estimation model typically produces higher quality scores for higher bit rates of compressed video information, the G.1070 model may produce a relatively high quality score for the complex video scene, even though the perceptual quality of that video scene may actually be low. Accordingly, in certain cases, the G.1070 video quality estimation model may either underestimate or overestimate the perceptual quality of video scenes, disadvantageously producing quality scores that may not correlate well with subjective quality scores of the end user.
It would therefore be desirable to have systems and methods of perceptual quality monitoring of video information, communications, and entertainment that avoid at least some of the drawbacks of the G.1070 model discussed above.